{"technology":{"slug":"climate-science","name":"Climate Science","description":"Climate change research, modeling, and mitigation. Covers atmospheric science, carbon capture, climate modeling, tipping points, and adaptation strategies.","discipline":"Earth Science / Environmental","icon":"🌍"},"lastUpdated":"2026-04-11T06:42:28.336Z","articleCount":15,"articles":[{"id":"oa-W2024966118","title":"An Overview of CMIP5 and the Experiment Design","authors":"Karl E. Taylor, Ronald J. Stouffer, Gerald A. Meehl","journal":"Bulletin of the American Meteorological Society","pubDate":"2011-10-07","doi":"10.1175/bams-d-11-00094.1","abstract":"The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2024966118","citationCount":14688,"isOpenAccess":true,"pdfUrl":"https://journals.ametsoc.org/downloadpdf/journals/bams/93/4/bams-d-11-00094.1.pdf"},{"id":"oa-W1996653992","title":"The Potential to Narrow Uncertainty in Regional Climate Predictions","authors":"Ed Hawkins, Rowan Sutton","journal":"Bulletin of the American Meteorological Society","pubDate":"2009-04-01","doi":"10.1175/2009bams2607.1","abstract":"Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare (a) the cost of various degrees of adaptation, given current levels of uncertainty and (b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W1996653992","citationCount":2712,"isOpenAccess":true,"pdfUrl":"https://journals.ametsoc.org/downloadpdf/journals/bams/90/8/2009bams2607_1.pdf"},{"id":"oa-W2088571921","title":"Comparing niche‐ and process‐based models to reduce prediction uncertainty in species range shifts under climate change","authors":"Xavier Morin, Wilfried Thuiller","journal":"Ecology","pubDate":"2009-05-01","doi":"10.1890/08-0134.1","abstract":"Obtaining reliable predictions of species range shifts under climate change is a crucial challenge for ecologists and stakeholders. At the continental scale, niche-based models have been widely used in the last 10 years to predict the potential impacts of climate change on species distributions all over the world, although these models do not include any mechanistic relationships. In contrast, species-specific, process-based predictions remain scarce at the continental scale. This is regrettable because to secure relevant and accurate predictions it is always desirable to compare predictions derived from different kinds of models applied independently to the same set of species and using the same raw data. Here we compare predictions of range shifts under climate change scenarios for 2100 derived from niche-based models with those of a process-based model for 15 North American boreal and temperate tree species. A general pattern emerged from our comparisons: niche-based models tend to predict a stronger level of extinction and a greater proportion of colonization than the process-based model. This result likely arises because niche-based models do not take phenotypic plasticity and local adaptation into account. Nevertheless, as the two kinds of models rely on different assumptions, their complementarity is revealed by common findings. Both modeling approaches highlight a major potential limitation on species tracking their climatic niche because of migration constraints and identify similar zones where species extirpation is likely. Such convergent predictions from models built on very different principles provide a useful way to offset uncertainties at the continental scale. This study shows that the use in concert of both approaches with their own caveats and advantages is crucial to obtain more robust results and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2088571921","citationCount":483,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2132411008","title":"Global climate change and soil carbon stocks; predictions from two contrasting models for the turnover of organic carbon in soil","authors":"Chris Jones, C. L. McConnell, K. Coleman, Peter M. Cox, Pete Falloon, D. S. Jenkinson, D. S. Powlson","journal":"Global Change Biology","pubDate":"2004-12-02","doi":"10.1111/j.1365-2486.2004.00885.x","abstract":"Abstract Enhanced release of CO 2 to the atmosphere from soil organic carbon as a result of increased temperatures may lead to a positive feedback between climate change and the carbon cycle, resulting in much higher CO 2 levels and accelerated global warming. However, the magnitude of this effect is uncertain and critically dependent on how the decomposition of soil organic C (heterotrophic respiration) responds to changes in climate. Previous studies with the Hadley Centre's coupled climate–carbon cycle general circulation model (GCM) (HadCM3LC) used a simple, single‐pool soil carbon model to simulate the response. Here we present results from numerical simulations that use the more sophisticated ‘RothC’ multipool soil carbon model, driven with the same climate data. The results show strong similarities in the behaviour of the two models, although RothC tends to simulate slightly smaller changes in global soil carbon stocks for the same forcing. RothC simulates global soil carbon stocks decreasing by 54 Gt C by 2100 in a climate change simulation compared with an 80 Gt C decrease in HadCM3LC. The multipool carbon dynamics of RothC cause it to exhibit a slower magnitude of transient response to both increased organic carbon inputs and changes in climate. We conclude that the projection of a positive feedback between climate and carbon cycle is robust, but the magnitude of the feedback is dependent on the structure of the soil carbon model.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2132411008","citationCount":396,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2165754634","title":"Effects of climate change and elevated CO2 on cropping systems: model predictions at two Italian locations","authors":"Francesco N. Tubiello, Marcello Donatelli, Cynthia Rosenzweig, Claudio O. Stöckle","journal":"European Journal of Agronomy","pubDate":"2000-07-01","doi":"10.1016/s1161-0301(00)00073-3","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2165754634","citationCount":365,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2008384755","title":"Climate Change and Risk of Leishmaniasis in North America: Predictions from Ecological Niche Models of Vector and Reservoir Species","authors":"Camila González, Ophelia Wang, Stavana E. Strutz, Constantino González‐Salazar, Víctor Sánchez‐Cordero, Sahotra Sarkar","journal":"PLoS neglected tropical diseases","pubDate":"2010-01-19","doi":"10.1371/journal.pntd.0000585","abstract":"These models predict that climate change will exacerbate the ecological risk of human exposure to leishmaniasis in areas outside its present range in the United States and, possibly, in parts of southern Canada. This prediction suggests the adoption of measures such as surveillance for leishmaniasis north of Texas as disease cases spread northwards. Potential vector and reservoir control strategies-besides direct intervention in disease cases-should also be further investigated.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2008384755","citationCount":350,"isOpenAccess":true,"pdfUrl":"https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0000585&type=printable"},{"id":"oa-W2171633038","title":"Is the future blue-green? A review of the current model predictions of how climate change could affect pelagic freshwater cyanobacteria","authors":"J. Alex Elliott","journal":"Water Research","pubDate":"2011-12-15","doi":"10.1016/j.watres.2011.12.018","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2171633038","citationCount":306,"isOpenAccess":true,"pdfUrl":"https://www.sciencedirect.com/science/article/pii/S0043135411007901"},{"id":"oa-W2112933898","title":"Should we believe model predictions of future climate change?","authors":"Reto Knutti","journal":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","pubDate":"2008-09-25","doi":"10.1098/rsta.2008.0169","abstract":"Predictions of future climate are based on elaborate numerical computer models. As computational capacity increases and better observations become available, one would expect the model predictions to become more reliable. However, are they really improving, and how do we know? This paper discusses how current climate models are evaluated, why and where scientists have confidence in their models, how uncertainty in predictions can be quantified, and why models often tend to converge on what we observe but not on what we predict. Furthermore, it outlines some strategies on how the climate modelling community may overcome some of the current deficiencies in the attempt to provide useful information to the public and policy-makers.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2112933898","citationCount":285,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2515542550","title":"Genetically informed ecological niche models improve climate change predictions","authors":"Dana H. Ikeda, Tamara Max, Gerard J. Allan, Matthew K. Lau, Stephen M. Shuster, Thomas G. Whitham","journal":"Global Change Biology","pubDate":"2016-08-20","doi":"10.1111/gcb.13470","abstract":"We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2515542550","citationCount":232,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W1902889365","title":"Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts","authors":"Hyemi Kim, Peter J. Webster, Judith A. Curry","journal":"Geophysical Research Letters","pubDate":"2012-04-18","doi":"10.1029/2012gl051644","abstract":"This study assesses the CMIP5 decadal hindcast/forecast simulations of seven state‐of‐the‐art ocean‐atmosphere coupled models. Each decadal prediction consists of simulations over a 10 year period each of which are initialized every five years from climate states of 1960/1961 to 2005/2006. Most of the models overestimate trends, whereby the models predict less warming or even cooling in the earlier decades compared to observations and too much warming in recent decades. All models show high prediction skill for surface temperature over the Indian, North Atlantic and western Pacific Oceans where the externally forced component and low‐frequency climate variability is dominant. However, low prediction skill is found over the equatorial and North Pacific Ocean. The Atlantic Multidecadal Oscillation (AMO) index is predicted in most of the models with significant skill, while the Pacific Decadal Oscillation (PDO) index shows relatively low predictive skill. The multi‐model ensemble has in general better‐forecast quality than the single‐model systems for global mean surface temperature, AMO and PDO.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W1902889365","citationCount":225,"isOpenAccess":true,"pdfUrl":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2012GL051644"},{"id":"oa-W3009376987","title":"Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China","authors":"Zhenzhen Liu, Mingyang Li, Chao Li, Zhenzhen Liu","journal":"Forests","pubDate":"2020-03-09","doi":"10.3390/f11030302","abstract":"Climate change significantly influences changes in ecological phenomena and processes, such as species distribution and phenology, thus accelerating the rate of species extinction or prosperity. Climate change is considered to be one of the most important threats to global biodiversity in the 21st century and will pose significant challenges to biodiversity conservation in the future. The use of niche modelling to predict changes in the suitable distribution of species under climate change scenarios is becoming a hot topic of biological conservation. In this study, we use data from China’s National Forest Continuous Inventory as well as specimen collection data of Cunninghamia lanceolata (Lamb.) Hook to run optimized Maxent models to predict potential suitable distribution of the species in the present day, 2050s, and 2070s under different climate change scenarios in China. In the modeling process, the most important uncorrelated variables were chosen, and the sample-size-adjusted Akaike information criterion (AICc) was used to select the optimal combination of feature type and regularization multiplier. Variable selection reduced the number of variables used and the complexity of the model, and the use of the AICc reduced overfitting. Variables relating to precipitation were more important than temperature variables in predicting C. lanceolata distribution in the optimal model. The predicted suitable distribution areas of C. lanceolata were different for the different periods under different climate change scenarios, with the centroids showing a degree of northward movement. The suitable distribution area is predicted to become more fragmented in the future. Our results reveal the climate conditions required for the suitable distribution of C. lanceolata in China and the likely changes to its distribution pattern in the future, providing a scientific basis for the sustainable management, protection, and restoration of the suitable habitat of this economically important tree species in the context of climate change.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W3009376987","citationCount":220,"isOpenAccess":true,"pdfUrl":"https://www.mdpi.com/1999-4907/11/3/302/pdf?version=1583748325"},{"id":"oa-W2023971153","title":"Regional climate‐model predictions of extreme rainfall for a changing climate","authors":"Chris Huntingford, Richard Jones, Christel Prudhomme, Rob Lamb, J. H. C. Gash, David Jones","journal":"Quarterly Journal of the Royal Meteorological Society","pubDate":"2003-04-01","doi":"10.1256/qj.02.97","abstract":"Abstract Major floods occurred in the United Kingdom during autumn 2000. These were caused by a rapid sequence of heavy rainfall events that occurred over a period of many weeks leading to record‐breaking monthly‐to‐seasonal rainfall totals. The question was raised as to whether such rainfall events may be related to human‐induced climate change. Climate‐model predictions of future changes in mean precipitation behaviour are well established. However, to understand flooding requires an examination of predictions of extreme rainfall behaviour at a relatively small spatial scale. For three areas within the United Kingdom, output from a Hadley Centre regional climate model, ‘nested’ within one of its general‐circulation models, is compared with raingauge data averaged over these areas for the period 1961–1990. This shows that the modelling system is good at predicting the statistical likelihood of extreme rainfall events seen in historical data. This result holds for extreme rainfall totals over daily to monthly timescales. When the modelling system is used to predict changes in these extreme events resulting from atmospheric CO 2 concentrations that may be representative of the period 2080–2100, significant reductions in the return periods of such events are seen. For example, 30‐day rainfall totals, which happened in the recent past on average once in 20 years, are predicted to happen once in 3–5 years. An interpolation method based upon climate‐model output and incorporating raingauge data is used to estimate how rainfall extremes may have changed between the middle of the 19th century, and for a period centred on the year 2000. This also predicts that increased greenhouse gases have led to reduced return periods of extreme rainfall events for three sites of interest, though in this case the changes are not statistically significant. © Royal Meteorological Society, 2003. R. G. Jones's contribution is Crown copyright","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2023971153","citationCount":151,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2216845620","title":"Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model","authors":"Juan Chang, Genxu Wang, Tianxu Mao","journal":"Journal of Hydrology","pubDate":"2015-09-25","doi":"10.1016/j.jhydrol.2015.09.038","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2216845620","citationCount":136,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W1967971435","title":"Anthropocene changes in desert area: Sensitivity to climate model predictions","authors":"N. M. Mahowald","journal":"Geophysical Research Letters","pubDate":"2007-09-01","doi":"10.1029/2007gl030472","abstract":"Changes in desert area due to humans have important implications from a local, regional to global level. Here I focus on the latter in order to better understand estimated changes in desert dust aerosols and the associated iron deposition into oceans. Using 17 model simulations from the World Climate Research Programme's Coupled Model Intercomparison Project phase 3 multi‐model dataset and the BIOME4 equilibrium vegetation model, I estimate changes in desert dust source areas due to climate change and carbon dioxide fertilization. If I assume no carbon dioxide fertilization, the mean of the model predictions is that desert areas expand from the 1880s to the 2080s, due to increased aridity. If I allow for carbon dioxide fertilization, the desert areas become smaller. Thus better understanding carbon dioxide fertilization is important for predicting desert response to climate. There is substantial spread in the model simulation predictions for regional and global averages.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W1967971435","citationCount":118,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2129530276","title":"Probabilistic climate change predictions applying Bayesian model averaging","authors":"Seung‐Ki Min, Daniel Simonis, Andreas Hense","journal":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","pubDate":"2007-06-14","doi":"10.1098/rsta.2007.2070","abstract":"This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation-maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070-2099) SATs while there is only a little effect of Bayesian weighting on the 5-95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2129530276","citationCount":92,"isOpenAccess":false,"pdfUrl":""}],"links":{"web":"https://science-database.com/technology/climate-science","llms_txt":"https://science-database.com/technology/climate-science/llms.txt","api":"https://science-database.com/api/v1/technology/climate-science"}}